Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0

Chao Yung Hsu; Li Wei Kang; Hsin Yi Lin; Ru Hong Fu; Chih Yang Lin; Ming Fang Weng; Duan Yu Chen

doi:10.1109/ICASI.2018.8394552

Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0

Chao Yung Hsu
, Li Wei Kang
, Hsin Yi Lin
, Ru Hong Fu
, Chih Yang Lin
, Ming Fang Weng
, Duan Yu Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method.

Original language	English
Title of host publication	Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
Editors	Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	145-146
Number of pages	2
ISBN (Electronic)	9781538643426
DOIs	https://doi.org/10.1109/ICASI.2018.8394552
Publication status	Published - 2018 Jun 22
Externally published	Yes
Event	4th IEEE International Conference on Applied System Innovation, ICASI 2018 - Chiba, Japan Duration: 2018 Apr 13 → 2018 Apr 17

Publication series

Name	Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

Conference

Conference	4th IEEE International Conference on Applied System Innovation, ICASI 2018
Country/Territory	Japan
City	Chiba
Period	2018/04/13 → 2018/04/17

Keywords

Industry 4.0
depth image
identification tracking
smart manufacturing
steel 4.0
steel industry

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Energy Engineering and Power Technology
Control and Systems Engineering
Mechanical Engineering
Control and Optimization
Modelling and Simulation
Biomedical Engineering

Access to Document

10.1109/ICASI.2018.8394552

Cite this

Hsu, C. Y., Kang, L. W., Lin, H. Y., Fu, R. H., Lin, C. Y., Weng, M. F., & Chen, D. Y. (2018). Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. In A. D. K.-T. Lam, S. D. Prior, & T.-H. Meen (Eds.), Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018 (pp. 145-146). (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI.2018.8394552

Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. / Hsu, Chao Yung; Kang, Li Wei; Lin, Hsin Yi et al.
Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. ed. / Artde Donald Kin-Tak Lam; Stephen D. Prior; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. p. 145-146 (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hsu, CY, Kang, LW, Lin, HY, Fu, RH, Lin, CY, Weng, MF & Chen, DY 2018, Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. in ADK-T Lam, SD Prior & T-H Meen (eds), Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 145-146, 4th IEEE International Conference on Applied System Innovation, ICASI 2018, Chiba, Japan, 2018/04/13. https://doi.org/10.1109/ICASI.2018.8394552

Hsu CY, Kang LW, Lin HY, Fu RH, Lin CY, Weng MF et al. Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. In Lam ADKT, Prior SD, Meen TH, editors, Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 145-146. (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018). doi: 10.1109/ICASI.2018.8394552

Hsu, Chao Yung ; Kang, Li Wei ; Lin, Hsin Yi et al. / Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. editor / Artde Donald Kin-Tak Lam ; Stephen D. Prior ; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 145-146 (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018).

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abstract = "To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method.",

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AU - Hsu, Chao Yung

AU - Kang, Li Wei

AU - Lin, Hsin Yi

AU - Fu, Ru Hong

AU - Lin, Chih Yang

AU - Weng, Ming Fang

AU - Chen, Duan Yu

PY - 2018/6/22

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N2 - To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method.

AB - To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method.

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Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0

Abstract

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Keywords

ASJC Scopus subject areas

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